{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Glued Trees Algorithm\n",
    "### Introduction\n",
    "Consider a network of two mirrored binary trees connected to each other, where the outermost nodes of each tree are connected to two random nodes in the other tree. This structure will have $2n$ columns and $2^{n+1}-2$ nodes in total, as shown in the diagram below. Each node in the structure has a secret key in the form of a random bit string of size $2n$, and you are given oracular access to the network such that you can query a node using its key to get the keys of its neighbors. Your goal is, given the key of the entrance node, to find the key of the exit node as efficiently as possible.\n",
    "\n",
    "<center>\n",
    "<img src=\"figures/glued_trees_diagram.png\" style=\"width:80%\">\n",
    "<figcaption align = \"middle\">Example of a Glued Trees Structure</figcaption>\n",
    "</center>\n",
    "\n",
    "If you try to play this game yourself, or program an algorithm to do so, you'll quickly run into a major problem: since you don't know what specific nodes on the tree the interior keys correspond to, you will get lost within the structure once you reach the area between the two trees. There is no way to guarantee a solution to this problem—using a classical computer—that doesn't require you to check every node in the worst case.\n",
    "\n",
    "There is a way to solve this problem efficiently however, on the order of the total number of *columns* of the structure instead of the nodes. You just need to use a quantum computer! This paper [[1](#GluedTrees)] published in December 2023 describes a quantum approach to solving this algorithm by considering the structure as a system of coupled harmonic oscillators attached by springs. A quantum computer can use Hamiltonian simulation to simulate this classical system efficiently. If you apply a push to the oscillator representing the entrance node, and treat the interactions between nodes as queries, you can \"reach\" the exit node (trigger a spike in its oscillatory movement) in time $2n$, offering linear efficiency as opposed to exponential efficiency!\n",
    "\n",
    "While this notebook will be following the algorithm described by the 2023 paper above, it should be noted that this problem was first set by this paper from October 2002 [[2](#QuantumWalk)].\n",
    "\n",
    "The following code segment imports all the necessary libraries for this notebook. Both Qiskit and NetworkX are required for the `generate_pauli_list` function, but the Pauli lists for 10 and 20 qubits (the examples in this notebook) have already been generated and cached in the `glued_trees_cache.json` file. You may uncomment the two lines at the bottom of the code segment, however, if you would like to try recalculating the Pauli lists or calculating them for other qubit sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import cast\n",
    "from classiq import *\n",
    "from classiq.execution import ExecutionPreferences\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import json\n",
    "%config InlineBackend.figure_format = 'retina' # improve graph image quality\n",
    "#from qiskit.quantum_info import SparsePauliOp\n",
    "#import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantum Algorithm\n",
    "To model the columns of the glued trees structure as a system of coupled harmonic oscillators, we consider a matrix $\\mathbf{A}$ of size $N \\times N$ corresponding to the nodes of the glued trees structure, such that $N=2^{n+1}-2$ and $n$ is the number of columns of one of the two glued trees. This matrix is defined as $\\mathbf{A}:=3(\\mathbf{1}_N)-A$, where $A$ is the adjacency matrix of the glued trees system using any ordering. \n",
    "\n",
    "For demonstration purposes, we will be using a simple linear ordering of this adjacency matrix such that the entrance node is first and the exit node is last. This matrix will be symmetrical and take the following shape:\n",
    "$$\n",
    "\\mathbf{A} = \\begin{pmatrix}\n",
    "3 & -1 & -1 & 0 & \\cdots & \\cdots & \\cdots & \\cdots & 0 \\\\\n",
    "-1 & 3 & 0 & -1 & \\cdots & \\cdots & \\cdots & \\cdots & 0 \\\\\n",
    "-1 & 0 & 3 & 0 & \\cdots & \\cdots & \\cdots & \\cdots & 0 \\\\\n",
    "0 & -1 & 0 & 3 & \\cdots & \\cdots & \\cdots & \\cdots & 0 \\\\\n",
    "\\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots \\\\\n",
    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & 3 & 0 & -1 & 0 \\\\\n",
    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & 0 & 3 & 0 & -1 \\\\\n",
    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & -1 & 0 & 3 & -1 \\\\\n",
    "0 & 0 & 0 & 0 & \\cdots & 0 & -1 & -1 & 3\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "\n",
    "As shown in further detail in the paper, we can define a block Hamiltonian $\\mathbf{H}$ such that\n",
    "$$\n",
    "\\mathbf{H} := -\\begin{pmatrix}\n",
    "\\mathbf{0} & \\mathbf{B} \\\\\n",
    "\\mathbf{B}^† & \\mathbf{0}\n",
    "\\end{pmatrix}\n",
    "$$\n",
    "where $\\mathbf{B}$ is any $N \\times M$ matrix such that $\\mathbf{B}\\mathbf{B}^†=\\mathbf{A}$. However, to use this matrix $\\mathbf{H}$ for Hamiltonian simulation, it must have a size corresponding to a power of two, while $\\mathbf{A}$ is size $N \\times N$. We can deal with this by ensuring that $\\mathbf{B}$ is size $N \\times (N+4)$, so the resulting Hamiltonian $\\mathbf{H}$ is a square matrix with side length $2N+4 = 2(2^{n+1}-2)+4 = 2^{n+2}$. This means that a glued trees system with $n$ columns for one tree can be simulated using $n+2$ qubits.\n",
    "\n",
    "The following code segments contain all of the helper methods that relate to creating the matrix $\\mathbf{H}$ and processing it in a form that Classiq's `exponentiation_with_depth_constraint` function can understand.\n",
    "\n",
    "The `pauli_str_to_enums` and `pauli_list_to_hamiltonian` functions are taken from the Classiq documentation and convert the list of tuples input into a `PauliTerm` list, the input Classiq recognizes for its exponentiation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHAR_TO_STUCT_DICT = {\"I\": Pauli.I, \"X\": Pauli.X, \"Y\": Pauli.Y, \"Z\": Pauli.Z}\n",
    "\n",
    "def pauli_str_to_enums(pauli):\n",
    "    return [CHAR_TO_STUCT_DICT[s] for s in pauli]\n",
    "\n",
    "def pauli_list_to_hamiltonian(pauli_list):\n",
    "    return [\n",
    "        PauliTerm(\n",
    "            pauli=pauli_str_to_enums(pauli), coefficient=cast(complex, coeff).real\n",
    "        )\n",
    "        for pauli, coeff in pauli_list\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we generate the matrix $\\mathbf{A}$ by building the glued trees structure using the NetworkX library such that the nodes are labeled in order from the entrance to exit node and using the `nx.adjacency_matrix` function to generate an adjacency matrix using that ordering. We will decompose $\\mathbf{A}$ using [Cholesky decomposition](https://en.wikipedia.org/wiki/Cholesky_decomposition) to get a square matrix where its product with its conjugate transpose is equal to $\\mathbf{A}$. This matrix is the same size as $\\mathbf{A}$, however, so we must pad it with 4 columns of zeroes to get our matrix $\\mathbf{B}$ of size $N \\times (N+4)$ so $\\mathbf{H}$ has a size corresponding to a power of two. We can then create the block Hamiltonian with the proper size using $\\mathbf{B}$ and $\\mathbf{B}^†$, and generate its full Pauli list using Qiskit's `SparsePauliOp.from_operator` [[3](#SparsePauliOp)] function.\n",
    "\n",
    "All Pauli list outputs are cropped so they have no more than 200 terms. This is to ensure that the circuit depth is at a reasonable range (low thousands) given the limits of current quantum hardware. As a result of this approach, as the number of qubits increases, the accuracy of the Pauli list in comparison to the matrix it represents diminishes so the circuit depth remains roughly constant.\n",
    "\n",
    "The Pauli list representation of $\\mathbf{H}$ for qubit sizes small enough to calculate (13 qubits and lower) are generated and cropped using the `generate_pauli_list` function, which uses an ad hoc approach that tries to represent the Pauli list as a whole as accurately as possible using only the 200 ostensibly most relevant terms to simulating the system.\n",
    "\n",
    "The cropping algorithm, defined in the `crop_pauli_list` function, first selects 120 terms (60%) by going through each character position from the end to the start and picking the Pauli terms with the largest coefficients that contain each of the four possibilities ($I$, $X$, $Y$, $Z$) at that character position. If all positions are exhausted before reaching 120 terms, the algorithm takes another pass through the character positions until 120 are selected. The other 80 terms (40%) are selected by picking the 80 remaining terms with the largest coefficients. The algorithm balances important high-coefficient terms with diversity in the 200 selected terms.\n",
    "\n",
    "The Pauli lists for qubit sizes too large to fully calculate (greater than 13 qubits) are approximated by padding with the second character of the Pauli strings of the largest cropped Pauli list that can be generated in reasonable time, as this follows a pattern present in the vast majority of strings in the cropped Pauli list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_pauli_list(pauli, size):\n",
    "    if len(pauli) <= size:\n",
    "        return pauli\n",
    "    result = []\n",
    "    idx = 0\n",
    "    while len(result) < round(size*0.6):\n",
    "        for i in range(len(pauli[0][0])-1, -1, -1):\n",
    "            for k in pauli:\n",
    "                if k[0][i] == 'IXYZ'[idx%4] and k not in result:\n",
    "                    result.append(k)\n",
    "                    break\n",
    "            idx += 1\n",
    "    for i in pauli:\n",
    "        if len(result) >= size:\n",
    "            break\n",
    "        if i not in result:\n",
    "            result.append(i)\n",
    "    return result\n",
    "\n",
    "def generate_pauli_list(qubits):\n",
    "    dim = qubits-2\n",
    "    T1 = nx.balanced_tree(2, dim-1)\n",
    "    T2 = nx.relabel_nodes(T1, lambda x: 2**(dim+1)-3-x)\n",
    "    T = nx.union(T1, T2)\n",
    "    edges = {i:0 for i in range(2**dim-1, 2**(dim-1)+2**dim-1)}\n",
    "    for i in range(2**(dim-1)-1, 2**dim-1):\n",
    "        nums = [j for j in range(2**dim-1, 2**(dim-1)+2**dim-1) if edges[j] < 1]\n",
    "        if len(nums) == 0:\n",
    "            nums = [j for j in range(2**dim-1, 2**(dim-1)+2**dim-1) if edges[j] < 2]\n",
    "        vals = random.sample(nums, k=2)\n",
    "        for j in vals:\n",
    "            edges[j] += 1\n",
    "        T.add_edges_from([(i, vals[0]), (i, vals[1])])\n",
    "    A = 3*np.identity(2**(dim+1)-2)-np.array(nx.adjacency_matrix(T, nodelist=sorted(T.nodes())).todense())\n",
    "    B = np.hstack((np.linalg.cholesky(A), np.zeros((2**(dim+1)-2, 4))))\n",
    "    H = -np.block([[np.zeros((B.shape[0], B.shape[0])), B], [B.conj().T, np.zeros((B.shape[1], B.shape[1]))]])\n",
    "    c = SparsePauliOp.from_operator(H)\n",
    "    result = [(str(c.paulis[i]), c.coeffs[i].real) for i in range(len(c))]\n",
    "    return crop_pauli_list(sorted(result, key=lambda x: abs(x[1]), reverse=True), 200)\n",
    "\n",
    "def pauli_str(qubits, recalculate=False):\n",
    "    cache = json.load(open('glued_trees_cache.json', 'r'))\n",
    "    if not recalculate and str(qubits) in cache:\n",
    "        return cache[str(qubits)]\n",
    "    if qubits > 13:\n",
    "        return [(i[0][0]+i[0][1]*(qubits-len(i[0]))+i[0][1:], i[1]) for i in generate_pauli_list(13)]\n",
    "    return generate_pauli_list(qubits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to run our main execution function, `run_point`. This function takes in the number of qubits `qubits` and the time `t` to perform Hamiltonian simulation $e^{-it\\mathbf{H}}$ using the `exponentiation_with_depth_constraint` function in the Classiq software development kit. \n",
    "\n",
    "The `max_depth` parameter is set to 1400, which is around the range of the current limit for comprehensible results in state of the art quantum computers. The `num_shots` parameter is set to 8192 to give enough of room for significant spikes in a state to be apparent given the high number of total possible states.\n",
    "\n",
    "The resulting quantum state can be written as follows:\n",
    "$$\n",
    "\\begin{aligned}\n",
    "|\\psi(t)\\rangle &\\propto \\begin{pmatrix}\n",
    "\\dot{\\vec{x}}(t) \\\\\n",
    "i\\mathbf{B}^†\\vec{x}(t) \n",
    "\\end{pmatrix} \\\\\n",
    "\\begin{pmatrix}\n",
    "\\dot{\\vec{x}}(t) \\\\\n",
    "i\\mathbf{B}^†\\vec{x}(t) \n",
    "\\end{pmatrix} &= e^{-it\\mathbf{H}} \\begin{pmatrix}\n",
    "\\dot{\\vec{x}}(0) \\\\\n",
    "i\\mathbf{B}^†\\vec{x}(0) \n",
    "\\end{pmatrix}\n",
    "\\end{aligned}\n",
    "$$\n",
    "where $\\vec{x}(0)=(0,0,\\dots,0)^T$ and $\\dot{\\vec{x}}(0)=(1,0,\\dots,0)^T$ using a linear ordering of nodes. \n",
    "\n",
    "Since the speed of the entrance node oscillator $|\\dot{x}_1(t)|$ is represented by the quantum state $|0\\rangle$ and should have probability 1 at $t=0$, there is no specific state preparation necessary for this system. It should also be noted that since our matrix $\\mathbf{B}^†$ is padded with 4 rows of zeroes, the highest 4 quantum states do not correspond to the displacement or speed of any oscillator. This means that the quantum state representing the speed of the exit node oscillator $|\\dot{x}_N(t)|$, which is what we are most interested in, will correspond to $|N-1\\rangle=|2^{n+1}-3\\rangle$. We will be tracking this particular quantum state at time $t \\approx 2n$ expecting a spike, which represents the system of oscillators \"reaching\" the exit node from the initial push to the entrance node.\n",
    "\n",
    "We will run the `run_point` function through a helper function `run_range`, which executes it 13 times in total for a given qubit size, spanning from $t=2n-12$ to $t=2n+12$ in 2 second intervals. This range gives time to observe oscillation occurring at the state value corresponding to $|\\dot{x}_N(t)|$ while also being close enough around $t=2n$, the time where we are expecting a spike."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_point(qubits, t, pauli_list):\n",
    "    @qfunc\n",
    "    def main(state: Output[QArray[QBit]]) -> None:\n",
    "        allocate(len(pauli_list[0][0]), state)\n",
    "        exponentiation_with_depth_constraint(\n",
    "            pauli_list_to_hamiltonian(pauli_list),\n",
    "            evolution_coefficient=t,\n",
    "            max_depth=1400,\n",
    "            qbv=state,\n",
    "        )\n",
    "    execution_preferences = ExecutionPreferences(num_shots=8192)\n",
    "    model = set_execution_preferences(create_model(main), execution_preferences)\n",
    "    quantum_program = synthesize(model)\n",
    "    job = execute(quantum_program)\n",
    "    filename = str(qubits)+'-qubits/2n'+(str(t-2*qubits+4) if t < 2*qubits-4 else '+'+str(t-2*qubits+4))\n",
    "    json.dump(dict(job.result()[0].value), open('results/'+filename+'.json', 'w'))\n",
    "\n",
    "def run_range(qubits, recalculate=False):\n",
    "    pauli_list = pauli_str(qubits, recalculate)\n",
    "    for i in range(-12, 13, 2):\n",
    "        run_point(qubits, 2*qubits-4+i, pauli_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code segment displays the execution of `run_range` for 10 qubits ($n=8, N=510$), a higher qubit size that is still simulatable. In addition, it is low enough that its Pauli list can still be fully generated and cropped.\n",
    "\n",
    "This instance of the function should take a few minutes to run. As defined, it will use the cached Pauli list for 10 qubits in `glued_trees_cache.json`. If you would like to recalculate the Pauli list, ensure that you have uncommented and run the two lines at the top of this notebook and add `recalculate=True` as the second parameter to the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_range(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code segment, on the other hand, displays the execution of `run_range` for 20 qubits ($n=18, N=524286$), a qubit size that is still simulatable but where its Pauli list cannot be generated and is thus approximated using the Pauli list for 13 qubits.\n",
    "\n",
    "This instance of the function should take a few minutes to run if the cached Pauli list is used. Since this instance of the function requires generating the Pauli list for 13 qubits, which is very large, it will take much longer to run if you choose to recalculate the Pauli list.\n",
    "\n",
    "You can also view the `qmod` file for the execution of `run_point` for 20 qubits at $t=2n$, which has been saved in this directory as `glued_trees_example.qmod`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_range(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now graph the results we have generated using the simulator, which have been saved into directories in the `results` folder based on their qubit size. The `graph_results` function creates a plot of the proportion of shots for the qubit state $|N-1\\rangle$ corresponding to $|\\dot{x}_N(t)|$ for a given qubit size from $t=2n-12$ to $t=2n+12$ and saves it in the `figures` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_results(qubits):\n",
    "    states = [(str(i) if i < 0 else '+'+str(i)) for i in range(-12, 13, 2)]\n",
    "    times = [2*qubits-4+int(i) for i in states]\n",
    "    data = []\n",
    "    for i in states:\n",
    "        with open('results/'+str(qubits)+'-qubits'+'/2n'+i+'.json', 'r') as f:\n",
    "            j = json.load(f)\n",
    "            key = str('0'+bin(2**(qubits-1)-3)[2:])\n",
    "            data.append(j['counts'][key]/j['num_shots'] if key in j['counts'] else 0)\n",
    "    plt.plot(times, data)\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.ylabel(\"Proportion of |\"+str(2**(qubits-1)-3)+\"> shots\")\n",
    "    plt.title(r'Glued Trees System at $t \\approx 2n$ for '+str(qubits)+r' Qubits ($n='+str(qubits-2)+r'$)')\n",
    "    plt.savefig('figures/'+str(qubits)+'_qubits.png', bbox_inches='tight', dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code segment displays the graph for 10 qubits, a qubit size with a generated and cropped Pauli list. As expected, there is a large spike at $t=2n$ indicative of the initial push to the entrance node having \"reached\" the exit node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 455,
       "width": 576
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_results(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code segment displays the graph for 20 qubits, a qubit size with an approximated Pauli list. There is a clear spike just before $t=2n$ caused by the propagation from the entrance node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 455,
       "width": 594
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_results(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of our Pauli list cropping, the algorithm can also be run with reasonable accuracy on actual quantum hardware. Simply add one of Classiq's quantum hardware [cloud providers](https://docs.classiq.io/latest/reference-manual/platform/executor/cloud-providers/) as an execution preference in the `run_point` function.\n",
    "\n",
    "Perhaps the most interesting thing about the glued trees algorithm is that it is a relatively heavy case of using a quantum computer to gain an exponential advantage, usually requiring several executions at different time points to observe the intended result, but it can still be executed effectively on present-day quantum hardware due to the Pauli list cropping. I encourage you to try out the algorithm on both a simulator and quantum hardware for various qubit sizes! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "<a id=\"GluedTrees\">[1]</a>: [Babbush, Ryan and Berry, Dominic W. and Kothari, Robin and Somma, Rolando D. and Wiebe, Nathan. \"Exponential Quantum Speedup in Simulating Coupled Classical Oscillators.\" Phys. Rev. X 13, 041041 (2023)](https://journals.aps.org/prx/pdf/10.1103/PhysRevX.13.041041)\n",
    "\n",
    "<a id=\"QuantumWalk\">[2]</a>: [Childs, Andrew M. and Cleve, Richard and Deotto, Enrico and Farhi, Edward and Gutmann, Sam and Spielman, Daniel A. \"Exponential algorithmic speedup by a quantum walk.\" Proc. 35th ACM Symposium on Theory of Computing (STOC 2003), pp. 59-68](https://arxiv.org/pdf/quant-ph/0209131)\n",
    "\n",
    "<a id=\"SparsePauliOp\">[3]</a>: [SparsePauliOp (Qiskit)](https://docs.quantum.ibm.com/api/qiskit/qiskit.quantum_info.SparsePauliOp)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
